Image gradients (Sobel, Laplacian) in Computer Vision - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to read an image using OpenCV.

Computer Vision

import cv2

image = cv2.[1]('image.jpg', cv2.IMREAD_GRAYSCALE)

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AcvtColor

Bimwrite

Cimshow

Dimread

Attempts:

3 left

💡 Hint

Common Mistakes

Using cv2.imshow instead of cv2.imread

Using cv2.imwrite which saves images instead of reading

Using cv2.cvtColor which converts color spaces

The cv2.imread function reads an image from a file. Here, we read it in grayscale mode.

2fill in blank

medium

Complete the code to apply the Sobel operator to find the gradient in the x direction.

Computer Vision

sobelx = cv2.Sobel(image, cv2.CV_64F, [1], 0, ksize=3)

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A1

B0

C-1

D2

Attempts:

3 left

💡 Hint

Common Mistakes

Setting dx to 0 which means no derivative in x

Using negative values which are invalid here

Confusing dx and dy arguments

The third argument is dx, which is 1 to compute the gradient in the x direction.

3fill in blank

hard

Fix the error in the code to compute the Laplacian of the image.

Computer Vision

laplacian = cv2.Laplacian(image, [1], ksize=3)

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Acv2.CV_64F

Bcv2.COLOR_BGR2GRAY

Ccv2.CV_8U

Dcv2.IMREAD_COLOR

Attempts:

3 left

💡 Hint

Common Mistakes

Using color conversion codes instead of depth

Using 8-bit unsigned depth causing incorrect results

The depth should be cv2.CV_64F to avoid overflow and keep negative gradient values.

4fill in blank

hard

Fill in the blank to compute the gradient magnitude from Sobel x and y gradients.

Computer Vision

import numpy as np

magnitude = np.sqrt(np.square(sobelx) [1] np.square(sobely))
magnitude = np.uint8(magnitude)

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A-

B+

C*

D/

Attempts:

3 left

💡 Hint

Common Mistakes

Subtracting squares which is mathematically incorrect

Multiplying or dividing squares which does not give magnitude

The gradient magnitude is the square root of the sum of squares of x and y gradients.

5fill in blank

hard

Fill all three blanks to create a dictionary with keys as gradient types and values as their computed arrays.

Computer Vision

gradients = {
    '[1]': sobelx,
    '[2]': sobely,
    '[3]': laplacian
}

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ASobel_X

BSobel_Y

CLaplacian

DGradient

Attempts:

3 left

💡 Hint

Common Mistakes

Using generic keys like 'Gradient' for all

Mixing up keys and values

Not using strings as keys

The dictionary keys should clearly label each gradient type: 'Sobel_X', 'Sobel_Y', and 'Laplacian'.